Odin Analytica Case Study

Context

Overview

Odin Analytica is an early-stage traffic analytics venture where I focused on modular backend services, data pipelines, and versioned APIs—building reliable ingestion, SQL persistence, and integrations for client-facing applications.

At a glance

Product

Backend services, versioned REST APIs, and ETL pipelines for real-time data ingestion, validation, and client integrations.

Audience / need

Production systems needed reliable real-time ingestion, validation, and APIs that could handle noisy data while remaining maintainable an…

Ownership

Developed modular, testable backend services and versioned REST APIs for real-time data ingestion, validation, SQL persistence, and integ…

Result

~20% — Latency

Role

What I owned

Developed modular, testable backend services and versioned REST APIs for real-time data ingestion, validation, SQL persistence, and integration with client-facing applications.
Monitored and debugged production services, redesigning data ingestion with a publish-subscribe architecture to reduce end-to-end latency by 20% while improving reliability and scalability.
Collaborated with engineers and project stakeholders in an Agile environment, contributing to design reviews, API integrations, and technical documentation while translating product requirements into tested backend features.
Built modular, testable Python services, ETL pipelines, and versioned RESTful APIs, automating structured data ingestion and improving the reliability, maintainability, and scalability of production systems.
Improved backend performance by analyzing application logs, profiling data flows, and redesigning ingestion workflows using a publish-subscribe architecture, reducing end-to-end latency by 20% while improving production reliability and scalability.
Collaborated with software engineers, product managers, and non-technical stakeholders in an Agile environment, communicating technical tradeoffs, participating in design discussions, and translating business requirements into backend features, API integrations, and technical documentation.
Developed backend services and versioned REST APIs for real-time ingestion and system integrations, implementing API contracts and validation to handle noisy data and improve system reliability.
Optimized end-to-end pipeline through profiling, load testing, and bottleneck isolation, reducing latency by 20% while maintaining throughput under real-time streaming constraints.
Defined API contracts, schemas (JSON), and validation layers significantly reducing integration errors and debugging time.

Outcomes

Key results

~20%

Latency

End-to-end latency reduction via publish-subscribe redesign

Tools

Technology stack

Backend: Python · FastAPI
Data: SQL
Other: REST APIs · ETL · Publish-subscribe